Abstract
Background
Conduct disorder (CD) is a common syndrome with far-reaching effects. Risk factors
for the development of CD span social, psychological, and biological domains. Researchers
note that predictive models of CD are limited if the focus is on a single risk factor
or even a single domain. Machine learning methods are optimized for the extraction
of trends across multidomain data but have yet to be implemented in predicting the
development of CD.
Methods
Social (e.g., family, income), psychological (e.g., psychiatric, neuropsychological),
and biological (e.g., resting-state graph metrics) risk factors were measured using
data from the baseline visit of the Adolescent Brain Cognitive Development Study when
youth were 9 to 10 years old (N = 2368). Applying a feed-forward neural network machine learning method, risk factors
were used to predict CD diagnoses 2 years later.
Results
A model with factors that included social, psychological, and biological domains outperformed
models representing factors within any single domain, predicting the presence of a
CD diagnosis with 91.18% accuracy. Within each domain, certain factors stood out in
terms of their relationship to CD (social: lower parental monitoring, more aggression
in the household, lower income; psychological: greater attention-deficit/hyperactivity
disorder and oppositional defiant disorder symptoms, worse crystallized cognition
and card sorting performance; biological: disruptions in the topology of subcortical
and frontoparietal networks).
Conclusions
The development of an accurate, sensitive, and specific predictive model of CD has
the potential to aid in prevention and intervention efforts. Key risk factors for
CD appear best characterized as reflecting unpredictable, impulsive, deprived, and
emotional external and internal contexts.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Biological Psychiatry: Cognitive Neuroscience and NeuroimagingAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Diagnostic and Statistical Manual of Mental Disorders (DSM-5).American Psychiatric Publishing, Washington, DC2013
- Conduct disorder.Nat Rev Dis Primers. 2019; 5: 43
- The high societal costs of childhood conduct problems: Evidence from administrative records up to age 38 in a longitudinal birth cohort.J Child Psychol Psychiatry. 2018; 59: 703-710
- Development and etiology of disruptive and delinquent behavior.Annu Rev Clin Psychol. 2009; 5: 291-310
- Positive and negative parenting in conduct disorder with high versus low levels of callous–unemotional traits.Dev Psychopathol. 2021; 33: 980-991
- Socioeconomic status and antisocial behaviour among children and adolescents: A systematic review and meta-analysis.Clin Psychol Rev. 2015; 35: 47-55
- Life course persistent and adolescence limited conduct disorder in a nationally representative US sample: Prevalence, predictors, and outcomes.Soc Psychiatry Psychiatr Epidemiol. 2017; 52: 435-443
- Previous maltreatment and present mental health in a high-risk adolescent population [published correction appears in Child Abuse Negl 2019; 89:237.Child Abuse Negl. 2015; 45: 122-134
- Neuropsychological measures of executive function and antisocial behavior: A meta-analysis.Criminology. 2011; 49: 1063-1107
- ADHD, CD, and ODD: Systematic review of genetic and environmental risk factors.Res Dev Disabil. 2018; 82: 10-19
- Validity of DSM-IV conduct disorder in 4½–5-year-old children: A longitudinal epidemiological study.Am J Psychiatry. 2005; 162: 1108-1117
- Risk factors for conduct disorder and delinquency: Key findings from longitudinal studies.Can J Psychiatry. 2010; 55: 633-642
- A meta-analytic review of the relation between antisocial behavior and neuropsychological measures of executive function.Clin Psychol Rev. 2000; 20: 113-136
- Conduct disorder symptomatology is associated with an altered functional connectome in a large national youth sample [published online ahead of print Apr 14].Dev Psychopathol. 2021;
- Functional connectivity estimated from resting-state fMRI reveals selective alterations in male adolescents with pure conduct disorder.PLoS One. 2015; 10e0145668
- Multivoxel pattern analysis of structural MRI in children and adolescents with conduct disorder.Brain Imaging Behav. 2019; 13: 1273-1280
- Three dimensional convolutional neural network-based classification of conduct disorder with structural MRI.Brain Imaging Behav. 2020; 14: 2333-2340
- Distinguishing adolescents with conduct disorder from typically developing youngsters based on pattern classification of brain structural MRI.Front Hum Neurosci. 2018; 12: 152
- Classification of pure conduct disorder from healthy controls based on indices of brain networks during resting state.Med Biol Eng Comput. 2020; 58: 2071-2082
- Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals.Comput Methods Programs Biomed. 2021; 200: 105941
- Longitudinal prediction of disruptive behavior disorders in adolescent males from multiple risk domains.Child Psychiatry Hum Dev. 2013; 44: 561-572
- Developmental trajectories of conduct problems and cumulative risk from early childhood to adolescence.J Youth Adolesc. 2019; 48: 181-198
- Current perspectives on conduct disorder.Curr Psychiatry Rep. 2006; 8: 59-72
- Machine learning approaches for clinical psychology and psychiatry.Annu Rev Clin Psychol. 2018; 14: 91-118
- Machine learning with neuroimaging: Evaluating its applications in psychiatry.Biol Psychiatry Cogn Neurosci Neuroimaging. 2020; 5: 791-798
- Choosing prediction over explanation in psychology: Lessons from machine learning.Perspect Psychol Sci. 2017; 12: 1100-1122
- Identifying mental health status using deep neural network trained by visual metrics.Transl Psychiatry. 2020; 10: 430
- Machine learning: Trends, perspectives, and prospects.Science. 2015; 349: 255-260
- Deep learning.Nature. 2015; 521: 436-444
- Opening the black box of neural networks: Methods for interpreting neural network models in clinical applications.Ann Transl Med. 2018; 6: 216
- Image processing and analysis methods for the Adolescent Brain Cognitive Development Study.Neuroimage. 2019; 202: 116091
- The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design.Dev Cogn Neurosci. 2018; 32: 30-42
- Conduct Disorders in Childhood and Adolescence.Second Edition. Sage Publications, Thousand Oaks, CA1995
- Oppositional defiant and conduct disorder: A review of the past 10 years, Part I.J Am Acad Child Adolesc Psychiatry. 2000; 39: 1468-1484
- Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy.Psychol Rev. 1993; 100: 674-701
- Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime, Version 2013 (K-SADS-PL).Western Psychiatric Institute and Clinic, Pittsburgh, PA2013
- IV. NIH Toolbox Cognition Battery (CB): Measuring language (vocabulary comprehension and reading decoding).Monogr Soc Res Child Dev. 2013; 78: 49-69
- VIII. NIH Toolbox Cognition Battery (CB): Composite scores of crystallized, fluid, and overall cognition.Monogr Soc Res Child Dev. 2013; 78: 119-132
- Disorganized amygdala networks in conduct-disordered juvenile offenders with callous-unemotional traits.Biol Psychiatry. 2017; 82: 283-293
- Differential relations between juvenile psychopathic traits and resting state network connectivity.Hum Brain Mapp. 2015; 36: 2396-2405
- Disrupted reinforcement signaling in the orbitofrontal cortex and caudate in youths with conduct disorder or oppositional defiant disorder and a high level of psychopathic traits.Am J Psychiatry. 2011; 168: 152-162
- Disrupted default mode network connectivity in male adolescents with conduct disorder.Brain Imaging Behav. 2016; 10: 995-1003
- The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites.Dev Cogn Neurosci. 2018; 32: 43-54
- Abnormal anatomical connectivity between the amygdala and orbitofrontal cortex in conduct disorder.PLoS One. 2012; 7e48789
- Package ‘nnet’. R Package Version 7..(Available at:)
- Review on methods to fix number of hidden neurons in neural networks.Math Probl Eng. 2013; 2013: 1-11
- Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions.IEEE Trans Neural Netw. 1998; 9: 224-229
Vujicic T, Matijevic T, Ljucovic J, Balota A, Sevarac Z (2016): Comparative analysis of methods for determining number of hidden neurons in artificial neural network. Central European Conference on Information and Intelligent Systems. Faculty of Organization and Informatics, Varaždin, Croatia, 219–223.
- Adaptive weighted learning for unbalanced multicategory classification.Biometrics. 2009; 65: 159-168
- SMOTE: synthetic minority over-sampling technique.J Artif Intell Res. 2002; 16: 321-357
- ROSE: A package for binary imbalanced learning.The R Journal. 2014; 6: 79-89
- Training and assessing classification rules with imbalanced data.Data Min Knowl Discov. 2014; 28: 92-122
- When optimism hurts: Inflated predictions in psychiatric neuroimaging.Biol Psychiatry. 2014; 75: 746-748
- Training with noise is equivalent to Tikhonov regularization.Neural Comput. 1995; 7: 108-116
- An overview of overfitting and its solutions.J Phys Conf S. 2019; 1168022022
- High-dimensional methods and inference on structural and treatment effects.J Econ Perspect. 2014; 28: 29-50
- Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap.Comp Stat Data Anal. 2009; 53: 3735-3745
- An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data.Ecol Modell. 2004; 178: 389-397
- The relationship between parental knowledge and monitoring and child and adolescent conduct problems: A 10-year update.Clin Child Fam Psychol Rev. 2011; 14: 377-398
- The relationship between parenting and delinquency: A meta-analysis.J Abnorm Child Psychol. 2009; 37: 749-775
- Which boys will fare worse? Early predictors of the onset of conduct disorder in a six-year longitudinal study.J Am Acad Child Adolesc Psychiatry. 1995; 34: 499-509
- Which better predicts conduct problems? The relationship of trajectories of conduct problems with ODD and ADHD symptoms from childhood into adolescence.J Child Psychol Psychiatry. 2007; 48: 601-608
- The long-term longitudinal course of oppositional defiant disorder and conduct disorder in ADHD boys: Findings from a controlled 10-year prospective longitudinal follow-up study.Psychol Med. 2008; 38: 1027-1036
- A common genetic factor explains the covariation among ADHD ODD and CD symptoms in 9–10 year old boys and girls.J Abnorm Child Psychol. 2009; 37: 153-167
- Evidence for a multi-dimensional latent structural model of externalizing disorders.J Abnorm Child Psychol. 2013; 41: 223-237
- Patterns of heterotypic continuity associated with the cross-sectional correlational structure of prevalent mental disorders in adults.JAMA Psychiatry. 2014; 71: 989-996
- The roles of orbital frontal cortex in the modulation of antisocial behavior.Brain Cogn. 2004; 55: 198-208
- Decision making and executive function in male adolescents with early-onset or adolescence-onset conduct disorder and control subjects.Biol Psychiatry. 2009; 66: 162-168
- The neuropsychology of conduct disorder.Dev Psychopathol. 1993; 5: 135-151
- Impaired neurocognitive functions affect social learning processes in oppositional defiant disorder and conduct disorder: Implications for interventions.Clin Child Fam Psychol Rev. 2012; 15: 234-246
- Brain structure abnormalities in early-onset and adolescent-onset conduct disorder.Am J Psychiatry. 2011; 168: 624-633
- A systematic review and meta-analysis of neuroimaging in oppositional defiant disorder (ODD) and conduct disorder (CD) taking attention-deficit hyperactivity disorder (ADHD) into account.Neuropsychol Rev. 2016; 26: 44-72
- Recent neuro-imaging findings with respect to conduct disorder, callous-unemotional traits and psychopathy.Curr Opin Psychiatry. 2020; 33: 45-50
- Lifetime prevalence of mental disorders in U.S. adolescents: Results from the National Comorbidity Survey Replication—Adolescent Supplement (NCS-A).J Am Acad Child Adolesc Psychiatry. 2010; 49: 980-989
- Population modeling with machine learning can enhance measures of mental health.Gigascience. 2021; 10giab071
- Reliability and stability challenges in ABCD task fMRI data.bioRxiv. 2021; https://doi.org/10.1101/2021.10.08.463750
- ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.Transl Psychiatry. 2020; 10: 100
- Classification and treatment of antisocial individuals: From behavior to biocognition.Neurosci Biobehav Rev. 2018; 91: 259-277
Teplin LA, Abram KM, McClelland GM, Mericle AA, Dulcan MK, Washburn JJ (2006): Psychiatric disorders of youth in detention. Juvenile Justice Bulletin. Office of Juvenile Justice and Delinquency Prevention. Available at: https://ojjdp.ojp.gov/sites/g/files/xyckuh176/files/pubs/246824.pdf. Accessed October 3, 2021.
- Toward targeted interventions: Examining the science behind interventions for youth who offend.Annu Rev Criminol. 2022; 5: 345-369
Article info
Publication history
Published online: February 22, 2022
Accepted:
February 8,
2022
Received in revised form:
January 10,
2022
Received:
November 18,
2021
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2022 Society of Biological Psychiatry.
